An Exploration of Pursuing Professional Explorations

My Data Science Job Search: August - October, 2020

Rohan Lewis

2020.09.29 (Updated 2020.10.06)

I began applying for Information Technology jobs on August 11th, 2020. I used LinkedIn as my primary source of information. I varied between large and small companies, EasyApply and normal applications via company job application portals or website submissions, and various locations across the US. I even applied to a job in Sydney, Australia (by accident) and a job in Toronto, Canada.

I saved my application information in an Excel Spreadsheet as I applied. I decided to use it to practice some visualization techniques.

#General Packages.
from datetime import date
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re

#Specific Packages
from wordcloud import WordCloud
import bar_chart_race as bcr
from IPython.display import Video
import plotly.graph_objects as go
from pywaffle import Waffle

#Read data.
df = pd.read_excel(r'D:\Data Science\Completed\Jobs\Jobs.xlsx')
lat_lons = pd.read_excel(r'D:\Data Science\Completed\Jobs\uscities.xlsx')

#Convert datetime columns to date.
df['Date_Applied'] = df['Date_Applied'].apply(lambda x: x.date())
df['Rejection_Email'] = df['Rejection_Email'].apply(lambda x: x.date())
df['Viewed_Email'] = df['Viewed_Email'].apply(lambda x: x.date())

#Current Numbers
print("As of " + date.today().strftime("%A, %B %d, %Y") + ", I have applied to " + str(df.shape[0]) + " jobs.")

As of Tuesday, October 06, 2020, I have applied to 469 jobs.

Job Title

This section looks at the specific job titles.

1. I eliminated all non alphabetical characters.


2. A recurring theme in job titles was numbering, such as "Data Analyst II" or "Data Scientist III". Since I had already eliminated numbers, I needed to eliminate Roman Numerals (only I, II, and III).


3. I had some false negatives, so I fixed those.
1. "AR VR" was changed back to "AR/VR" for one title.
2. "C C" was changed back to "C2C" for one title.
3. "Microsoft" was changed to "Microsoft-365" for one title.
4. "Non IT" was changed back to "Non-IT" for one title.


4. Words were then tallied.

Word Frequencies

Below are the top 10 and bottom 10 (some of the single occuring) words from the job titles.

#Combine all job titles into one string.
jobs_string = ' '.join(df['Title'])
#Only letters are useful.
regex = re.compile('[^a-zA-Z]')
#Remove all non letters, and remove ' I ', ' II ', ' III '.
jobs_string = regex.sub(' ', jobs_string).replace(' I ',' ').replace(' II ',' ').replace(' III ',' ')
#Specific Replacement.
jobs_string = jobs_string.replace('AR VR','AR/VR').replace('C C', 'C2C').replace('Microsoft', 'Microsoft-365').replace('Non IT', 'Non-IT')

#Create a frequency distribution of all words in the job titles.
jobs_dict = {}
jobs_words = jobs_string.split()
for word in jobs_words :
    if word not in jobs_dict.keys() :
        jobs_dict[word] = 1
    else :
        jobs_dict[word] += 1

#Convert frequency distribution to dataframe, sort by frequency.
jobs_df = pd.DataFrame({'Word' : list(jobs_dict.keys()),
                       'Count' : list(jobs_dict.values())}).sort_values(by = 'Count', ascending = False, axis = 0).reset_index(drop = True)
jobs_df.head(10)

	Word	Count
0	Data	426
1	Scientist	272
2	Analyst	111
3	Engineer	65
4	Learning	47
5	Machine	47
6	Science	21
7	Analytics	17
8	Research	16
9	Developer	14

jobs_df.tail(10)

	Word	Count
175	Training	1
176	Bioinformaticist	1
177	Card	1
178	Green	1
179	or	1
180	Citizens	1
181	Local	1
182	AR/VR	1
183	Non-IT	1
184	BI	1

Word Cloud

I thought a word cloud would be a fun representation to look at the job titles. The proportional size has been rescaled.

jobs_wc = WordCloud(background_color = 'white',
                    max_words = 300,
                    collocations = False,
                    relative_scaling = 0)
jobs_wc.generate(jobs_string)
plt.figure(figsize = (15, 11))

plt.imshow(jobs_wc, interpolation = 'bilinear')
plt.axis('off');

Companies

Some companies are hiring heavily. Some are recruiting and staffing agencies for others.

Once my application was in a system on a particular company's career portal page, it was easy to reapply. I used this quite a bit for companies like Amazon, Google, MITRE, and PayPal.

I used LinkedIn's EasyApply for many applications as well.

For the others, I sometimes applied as a guest, sometimes only had to upload my resume and cover, sometimes had to go through a 20 minute ordeal just for one opening. It varied. ¯_( ͡° ͜ʖ ͡°)_/¯

Company Applications by Date

See Appendix for full table of cumulative applications by company and date, sorted alphabetically and chronologically, respectively.

#List of all companies.
companies = df['Company'].unique()
#Dates from first application to today.
date_index = pd.date_range(start = min(df['Date_Applied']), end = date.today())

#Create new data frame of 0s.
application_df = pd.DataFrame(index = date_index, columns = companies).fillna(0)

#Create cumulative count of job applications by company and date.
for i in range(len(df)) :
    company = df.iloc[i, 1]
    date_app = df.iloc[i, 7]
    application_df.loc[date_app:, company] += 1

#Alphabetical
application_df = application_df.reindex(sorted(application_df.columns), axis=1)

#Total number of applications to each company.
cumulative_app_count = application_df.iloc[[-1]]
major_companies = cumulative_app_count.columns[(cumulative_app_count >= 5).iloc[0]]
minor_companies = cumulative_app_count.columns[(cumulative_app_count < 5).iloc[0]].tolist()

#Create a dummy company called 'Other', containing all companies with less than 4 applications.
major_df = pd.DataFrame.copy(application_df)
major_df['Other'] = major_df[minor_companies].sum(axis = 1)
major_df.drop(minor_companies, axis = 1, inplace = True)

#Check number of bars to include.
#major_df.shape

Bar Chart Race

Below is an animation of the above data. Bar chart races run more smoothly with larger numbers, like population, or monetary amounts, over longer periods of time. But I am happy the way this turned out.

bold_colors = ['#f0f0f0', '#3cb44b', '#e6194b', '#fffac8', '#9a6324', '#e6beff', '#fabebe', '#000075',
               '#ffe119', '#008080', '#ffffff', '#4363d8', '#bcf60c', '#46f0f0', '#911eb4', '#800000',
               '#f032e6', '#808000', '#ffd8b1', '#f58231', '#aaffc3', '#000000', '#ca1699']

apps = bcr.bar_chart_race(df = major_df,
                          filename = 'applications.mp4',
                          orientation = 'h',
                          sort = 'desc',
                          n_bars = 15,
                          cmap = bold_colors[2:22],
                          filter_column_colors = False,
                          period_fmt = '%B %d, %Y',
                          period_label = {'x': 0.99,
                                          'y': 0.155,
                                          'ha': 'right',
                                          'size': 14},
                          period_summary_func = lambda v, r: {'x': 0.99,
                                                              'y': 0.05,
                                                              's': f"{v.sum():,.0f} applications completed.\nCompanies with less than 4 applications are grouped in 'Other'.",
                                                              'ha': 'right',
                                                              'size': 9,
                                                              'weight': 'normal'},
                          title = 'Total Number of Jobs Applied to by Company and Date',
                          steps_per_period = 10)

Video("applications.mp4")

Job Location

Some slight modifications were made from LinkedIn data during the application process:

1. For Curate Partners, a job location was changed from Raleigh-Durham-Chapel Hill Area to Raleigh.
2. For Parker & Lynch, a job location was changed from Orange County to Los Angeles.
3. For Synectics, a job location was changed from Greater Chicago to Chicago.
4. For several companies, job locations were changed from "Greater" or "Metropolitan" of a city to that city.

In addition, for Common App, the job location was changed from none specified to Arlington. I did not learn about their opening from LinkedIn.

By City

Remote Locations

Several jobs were advertised with no city, only remote.

df[df['City'] == "Remote"]

	Title	Company	Size	City	State_abbv	State	Date_Posted	Date_Applied	Rejection_Email	Viewed_Email	CoID	JobID	URL
192	Data Scientist	Pilot Flying J	10001+	Remote	NaN	NaN	2020-09-04 00:00:00	2020-09-06	2020-09-17	NaT	NaN	2.019452e+09	https://www.linkedin.com/jobs/search/?currentJ...
413	Forward Deployed Data Scientist	Cresta	11-50	Remote	NaN	NaN	2020-09-29 00:00:00	2020-09-30	NaT	NaT	NaN	2.155368e+09	https://www.linkedin.com/jobs/search/?currentJ...
420	Data Scientist	White Ops	51-200	Remote	NaN	NaN	> 1 week	2020-09-30	NaT	NaT	NaN	2.023661e+09	https://www.linkedin.com/jobs/search/?currentJ...
425	Platform Data Engineer	Demyst	51-200	Remote	NaN	NaN	2020-10-01 00:00:00	2020-10-01	NaT	NaT	NaN	2.152177e+09	https://www.linkedin.com/jobs/search/?currentJ...
437	Intern - AI/Machine Learning	Seagate Technology	10001+	Remote	OR	Oregon	2020-09-30 00:00:00	2020-10-01	NaT	NaT	201783	2.183372e+09	https://www.linkedin.com/jobs/search/?currentJ...

I retrieved their office locations and manually entered them.

#Manual City, State_abbv, and State entry. 
dict_cs = {192: ("Knoxville", "TN", "Tennessee"),
           413: ("San Francisco", "CA", "California"),
           420: ("New York", "NY", "New York"),
           425: ("New York", "NY", "New York"),
           437: ("Portland", "OR", "Oregon")}

#Loop to manually enter missing City, State_abbv, and State entry for Remote locations.
for idx in dict_cs :
    temp_city = dict_cs[idx][0]
    temp_state_abbv = dict_cs[idx][1]
    temp_state = dict_cs[idx][2]
    df.at[idx, 'City'] = temp_city
    df.at[idx, 'State_abbv'] = temp_state_abbv
    df.at[idx, 'State'] = temp_state

Missing Cities

Several jobs I applied to were in cities not in the spreadsheet I downloaded.

#Select relevant columns.
df_loc = df[['City', 'State_abbv', 'State', 'Date_Applied']]

#Select only city, state, and location columns.
lat_lons = lat_lons[['city', 'state_id', 'lat', 'lng']]

#Count the number of applications.
city_tally = df_loc.groupby(['City', 'State_abbv', 'State']).count().reset_index()
#Merge to get latitude, longitude for each city.
merged = pd.merge(city_tally,
                  lat_lons,
                  how = 'left',
                  left_on = ['City', 'State_abbv'],
                  right_on = ['city', 'state_id'])



#Several cities are not in the list.
merged[merged['city'].isna()]

	City	State_abbv	State	Date_Applied	city	state_id	lat	lng
8	Bedford	MA	Massachusetts	4	NaN	NaN	NaN	NaN
21	Bridgewater	NJ	New Jersey	1	NaN	NaN	NaN	NaN
27	Center Valley	PA	Pennsylvania	1	NaN	NaN	NaN	NaN
39	Dallas - Ft. Worth	TX	Texas	5	NaN	NaN	NaN	NaN
83	Patuxent River	MD	Maryland	1	NaN	NaN	NaN	NaN
114	Sydney	AU	Australia	1	NaN	NaN	NaN	NaN
117	Toronto	CN	Canada	1	NaN	NaN	NaN	NaN
125	Weston	MA	Massachusetts	1	NaN	NaN	NaN	NaN

Manual Removal and Entry

Two were from Australia and Canada. For the others, I retrieved values from Google. An approximate average value was chosen for Dallas-Ft. Worth.

#Huxley job from Sydney, Australia was removed.
merged = merged[merged.State_abbv != 'AU']
#Prodigy Academy job from Toronto, Canada was removed.
merged = merged[merged.State_abbv != 'CN']

#Manual latitude and longitude entry. 
dict_loc = {8: (42.4906, -71.2760),
            21: (40.5940, -74.6049),
            27: (40.5294, -75.3937),
            39: (32.7598, -97.0646),
            83: (38.2773, -76.4229),
            125: (42.3668, -71.3031)}

#Loop to manually enter missing latitude and longitudes.
for idx in dict_loc :
    lat = dict_loc[idx][0]
    lon = dict_loc[idx][1]
    merged.at[idx, 'lat'] = lat
    merged.at[idx, 'lng'] = lon

#Rename columns and drop redundant columns    
merged = merged.rename(columns = {'Date_Applied' : 'Count',
                                  'lat': 'Latitude',
                                  'lng': 'Longitude'}).drop(['city', 'state_id'], axis = 1)

Mountain View

My left join was 11 off. I soon determined that two cities in California are named Mountain View.

merged[merged.City == 'Mountain View']

	City	State_abbv	State	Count	Latitude	Longitude
74	Mountain View	CA	California	11	37.4000	-122.0796
75	Mountain View	CA	California	11	38.0093	-122.1169

I removed the one not in the Bay Area. All cities, with their state, number of applications, and location, are displayed below.

merged = merged[(merged['Latitude'] != 38.0093) | (merged['Longitude'] != -122.1169)]
merged

	City	State_abbv	State	Count	Latitude	Longitude
0	Andover	MA	Massachusetts	1	42.6554	-71.1418
1	Arlington	VA	Virginia	5	38.8786	-77.1011
2	Ashburn	VA	Virginia	1	39.0300	-77.4711
3	Asheville	NC	North Carolina	1	35.5704	-82.5536
4	Atlanta	GA	Georgia	16	33.7627	-84.4224
...	...	...	...	...	...	...
123	Wellesley	MA	Massachusetts	6	42.3043	-71.2855
124	West Menlo Park	CA	California	2	37.4338	-122.2034
125	Weston	MA	Massachusetts	1	42.3668	-71.3031
126	Wilmington	DE	Delaware	1	39.7415	-75.5413
127	Woonsocket	RI	Rhode Island	1	42.0010	-71.4993

125 rows × 6 columns

By State

See Appendix for frequency by state.

#Group job applications by State, count them, reset the index, drop the date, and rename City to Count.
state_tally = merged[['Count', 'State_abbv', 'State']].groupby(['State_abbv', 'State']).sum().sort_values(by = 'Count', ascending = False, axis = 0).reset_index()

US Map

Below is an interactive map of the US. Applications are sorted by city and state.

#Singular or Plural.
def f(row) :
    if row['Count'] == 1 :
        string_val = ' application in '
    else :
        string_val = ' applications in '

    return string_val

#Number of applications per city as text.
merged['Text'] = merged['Count'].astype(str) + merged.apply(f, axis = 1) + merged['City'] + ', ' + merged['State_abbv'] + '.'

#Number of applications per state as text.
state_tally['Text'] = state_tally['Count'].astype(str) + state_tally.apply(f, axis = 1) + state_tally['State'] + '.'

#Color in states by number of applications.
state_map_data = go.Choropleth(locations = state_tally['State_abbv'],
                               z = state_tally['Count'],
                               text = state_tally['Text'],
                               hoverinfo = 'text',
                               locationmode = 'USA-states',
                               colorbar = {'title': "<b>Applications</b>",
                                           'thicknessmode': "pixels",
                                           'thickness': 70,
                                           'lenmode': "pixels",
                                           'len': 400,
                                           'titlefont': {'size': 16},
                                           'tickfont': {'size': 12},
                                           'tickvals': [0, 20, 40, 60, 80, 100]},
                               colorscale = 'Blues')

#Plot cities, size corresponds to number of applications.
city_map_data = go.Scattergeo(lon = merged['Longitude'],
                              lat = merged['Latitude'],
                              text = merged['Text'],
                              hoverinfo = 'text',
                              locationmode = 'USA-states',
                              marker = {'size': 10*np.sqrt(merged['Count']),
                                        'color': 'Darkgreen'})

data = [state_map_data, city_map_data]

fig = go.Figure(data = data)
fig.update_layout(title = {'text': 'Where I Have Applied (Hover for Cities and Count)',
                           'font': {'size': 30}},
                  geo_scope = 'usa',
                  width = 950,
                  height = 550)

Waffle Plot

Below is the distribution of applications by state.

#Sort by number of applications for each state.
waffle_data = state_tally[['State', 'Count']]

#Add a row for states with less than 3 applications.  'Other' also includes Australia and Japan.
waffle_data = waffle_data.append(pd.DataFrame({'State': ['Other'], 'Count': [2]})).reset_index(drop = True)

to_drop = []
#Add applications from states with less than 4 to 'Other'.
for i in waffle_data.index :
    if waffle_data.iloc[i]['Count'] < 4 :
        temp = waffle_data.iloc[i]['Count']
        waffle_data.at[33, 'Count'] += temp
        to_drop.append(i)

#Remove states with less than 3.  Change orientation of data.  
waffle_data = waffle_data.drop(labels = to_drop, axis = 0).reset_index(drop = True).set_index('State').transpose()
waffle_data.shape

(1, 20)

fig = plt.figure(FigureClass = Waffle,
                 rows = 14,
                 values = waffle_data.values.tolist()[0],
                 labels = waffle_data.columns.tolist(),
                 figsize = (14, 9),
                 colors = bold_colors[0:20],
                 legend = {'loc': 'upper left',
                           'ncol': 2,
                           'fontsize': 13})

Appendix

Company Applications by Date

pd.set_option('display.max_columns', None)
display(application_df)

	AETEA Information Technology	ALTEN	AP Professionals	ARYZTA	Accelere	Addison Professional Financial Search LLC	Aditi Consulting	Advanced Auto Parts	Age of Learning	Alexander Technology Group	Amazon	American Bureau of Shipping	Analytic Recruiting Inc.	Apex Systems	Artisan Talent	Ascii Group, LLC	Atlas Reasearch	Austin Fraser	Averity	BCG Digital Ventures	Bayside Solutions	Bear Cognition	Big Cloud	BlueAlly Services	Broadridge	Brooksource	Burtch Works	CBTS	CVS / Aetna	CarMax	CareHarmony	Caterpillar	CircleUp	Cisco	ClearBridge Technology Group	Clever Devices	Coit Group	Collabera, Inc.	Common App	CompuGain	CoreSite	Cornerstone Staffing Solutions, Inc.	Coursera	Crawford Thomas Recruiting	Cresta	Curate Partners	CybeCys, Inc.	CyberCoders	DMI	DataLab USA	Delphi-US, LLC	Demyst	Dick's Sporting Goods	Diversant, LLC	Dstillery	Edison	Eliassen Group	Enhance IT	Entech	Entelligence	EpicTec	Expedia	FICO	Fandango	FedEx	Fladger Associates	FleetCor Technologies, Inc.	Flexton	Flywheel Digital	ForgeRock	Forrest Solutions	Further Enterprise Solutions	Gap Inc.	GitHub	Good Apple	Google	Greater New York Insurance Companies	Greenphire	Gsquared Group	HIRECLOUT	HP	Harnham	Havas Media Group	Hays	Hired by Matrix, Inc	Hirewell	Homesnap	Horizon Media	Horizontal Talent	Huxley	IBM	IDR	IQVIA	ISO	Ibotta	Idexcel	Illumination Works	Innovative Systems Group	Intelliswift	International Consulting Associates, Inc.	JM Eagle	JPI	Jefferson Frank	Jobot	Jobspring Partners	KGS Technology Group, Inc	Kairos Living	Kenshoo	Komodo Health	LevelUP	LexisNexis	LinkedIn	LivePerson	LockerDome	M Science	MITRE	Macro Eyes	Magnifi	MaxisIT, Inc.	McKinley Marketing Partners	Media Assembly	Mesh Recruiting, LLC	Microsoft	MindPool, Inc.	Modis	Moodys Northwest Consulting	MotiveMetrics	Mount Indie	Next Insurance	Ntelicor	Nvidia	OkCupid	Olive	Onebridge	OpenArc, LLC.	Optello	Parker and Lynch	Paro.io	Patel Consulatants	Pathrise	PayPal	Pilot Flying J	Planet Pharma	Prime Team Partners	Proclinical Ltd.	Prodigy Education	Puls	Quadrant Resource	Radiansys, Inc.	Real	Rent-A-Center	Reply	Retail Solutions Inc.	SBS Creatix, LLC	Sand Cherry Associates	Sanjay Bharti, MD PLLC	Scale Media	Scion Staffing	Seagate Technology	Selling Simplified	Servpro Industries, LLC	ShootProof	SkyWater Search Partners	Sogeti	Sonder Inc.	Susquehanna International Group, LLP	Synectics Inc.	Synergis	Systecon North America	TRC Staffing Services, Inc.	Tech Observer	TechWorkers	Technology Ventures	Tencent	The AI Institute	The Equus Group	The Home Depot	The Jacobson Group	The Judge Group	The Lab Consulting	Topco Associates LLC	Toyoda Gosei Americas	Ursus, Inc.	Valassis	Via	Visible	Walgreens	White Ops	Wind River	Wish	Wonderlic	WorldLink US	Yoh, A Day & Zimmermann Company, LLC	zyBooks
2020-08-11	0	0	0	0	0	0	0	0	0	0	11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2020-08-12	0	0	0	0	0	0	0	0	0	0	11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2020-08-13	0	0	0	0	0	0	0	0	0	0	13	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	12	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
2020-08-14	0	0	0	0	0	0	0	0	0	0	13	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	12	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
2020-08-15	0	0	0	0	0	0	0	0	0	0	13	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	12	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
2020-08-16	0	0	0	0	0	0	0	0	0	0	13	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	12	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
2020-08-17	0	0	0	0	0	0	0	0	0	0	13	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	12	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
2020-08-18	0	0	0	0	0	0	0	0	0	0	13	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	12	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
2020-08-19	0	0	0	0	0	0	0	0	0	0	23	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	0	10	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	12	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
2020-08-20	0	0	0	0	0	0	0	0	0	0	23	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	9	0	10	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	12	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0
2020-08-21	0	0	0	0	0	0	0	0	0	0	25	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	9	0	10	0	0	0	0	4	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	7	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	12	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0
2020-08-22	0	0	0	0	0	0	0	0	0	0	26	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	13	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	9	0	10	0	0	0	0	4	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	3	0	0	0	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	13	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	2	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0
2020-08-23	0	0	0	0	0	0	0	0	0	0	26	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	13	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	9	0	10	0	0	0	0	4	0	0	0	0	0	0	0	0	0	2	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	3	0	0	0	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	13	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	3	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0
2020-08-24	0	0	0	0	0	0	0	0	0	0	26	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	13	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	9	0	10	0	0	0	0	4	0	0	0	0	0	0	0	0	0	2	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	3	0	0	0	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	13	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	3	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0
2020-08-25	0	0	0	0	0	0	0	0	0	0	26	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	13	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	9	0	10	0	0	0	0	4	0	0	0	0	0	0	0	0	0	2	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	3	0	0	0	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	13	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	3	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0
2020-08-26	0	0	0	0	0	0	0	0	0	0	26	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	13	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	9	0	10	0	0	0	0	4	0	0	0	0	0	0	0	0	0	2	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	3	0	0	0	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	13	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	3	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0
2020-08-27	0	0	0	0	0	0	0	0	0	0	26	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	13	0	0	0	0	3	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	9	0	10	0	0	0	0	4	0	0	0	0	0	0	0	0	0	2	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	3	0	0	4	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	14	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	3	0	0	0	0	0	0	1	0	0	1	0	1	2	0	0	0	0
2020-08-28	0	0	0	0	0	0	0	0	0	0	26	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	13	0	0	0	0	4	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	9	0	10	0	0	0	0	4	0	0	0	0	0	0	0	0	0	2	0	0	0	2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	3	0	0	4	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	14	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	0	5	0	0	0	0	0	0	1	0	0	1	0	1	2	0	0	0	0
2020-08-29	0	0	0	0	0	0	0	0	0	0	27	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	13	0	0	0	0	4	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	9	0	10	0	0	0	0	4	0	0	0	0	0	0	0	0	0	2	0	0	0	2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	3	0	0	4	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	14	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	0	5	0	0	0	0	0	0	1	0	0	1	0	1	2	0	0	0	0
2020-08-30	0	0	1	0	0	0	0	0	0	0	28	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	1	0	13	0	1	0	0	4	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	1	0	0	0	0	0	0	0	0	4	9	0	10	1	0	0	0	4	0	0	0	0	0	0	0	0	1	2	0	0	1	2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	3	1	0	5	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	14	0	1	0	0	1	1	0	0	0	1	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	1	1	1	0	0	0	1	1	0	0	5	0	0	0	0	0	0	1	0	0	1	0	1	4	0	0	3	0
2020-08-31	0	0	1	0	0	0	0	0	0	0	28	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	1	0	13	0	1	0	0	4	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	1	0	0	0	0	0	0	0	0	4	9	0	10	1	0	0	0	4	0	0	0	0	0	0	0	0	1	2	0	0	1	2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	3	1	0	5	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	14	0	1	0	0	1	1	0	0	0	1	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	1	1	1	0	0	0	1	1	0	0	5	0	0	0	0	0	0	1	0	0	1	0	1	4	0	0	3	0
2020-09-01	0	0	1	0	0	0	0	0	0	0	28	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	1	0	13	0	1	0	0	4	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	2	0	1	0	0	0	0	0	0	0	0	4	9	0	10	1	0	0	0	4	0	0	0	0	0	0	0	0	1	2	0	0	1	2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	3	1	0	5	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	14	0	1	0	0	1	1	0	0	0	1	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	1	1	1	0	0	0	1	1	0	0	5	0	0	0	0	0	0	1	0	0	1	0	1	4	0	0	3	0
2020-09-02	0	0	1	0	0	0	0	0	0	0	28	0	0	2	0	0	0	1	1	0	0	1	0	0	0	0	1	0	13	0	1	0	0	4	0	0	0	1	1	0	1	0	1	0	0	2	0	0	0	0	0	0	4	0	0	1	0	0	0	0	0	2	0	1	0	0	0	0	0	0	1	0	4	9	0	10	1	0	0	0	4	0	0	0	0	0	0	0	0	1	2	1	1	1	2	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	3	1	0	5	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1	14	0	1	0	0	1	1	0	0	0	1	0	0	0	0	1	0	2	0	1	0	0	0	0	0	0	1	1	1	0	0	0	1	1	1	0	5	0	0	0	0	0	0	1	1	0	1	0	1	4	1	0	3	1
2020-09-03	0	0	1	0	0	1	0	0	0	0	28	0	0	2	0	0	0	1	1	0	0	1	0	0	1	0	1	0	13	0	1	0	0	4	0	0	0	1	1	0	1	0	1	0	0	2	0	0	0	0	0	0	4	0	0	1	0	0	0	0	0	2	0	1	0	0	0	0	0	0	1	0	4	9	0	10	1	0	0	0	4	0	0	0	0	0	0	0	0	1	2	1	1	1	2	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	3	1	0	5	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1	14	0	1	0	0	1	1	0	0	0	1	0	0	0	0	1	0	2	0	1	0	0	0	0	0	0	1	1	1	0	0	0	1	1	1	0	5	0	0	0	0	0	0	1	1	0	1	0	1	4	1	0	3	1
2020-09-04	0	0	1	0	0	1	0	0	1	0	28	0	0	3	0	0	0	1	1	0	0	1	0	0	1	0	1	0	13	0	1	0	0	4	0	0	0	1	1	0	1	0	1	0	0	2	0	0	0	0	0	0	4	1	0	1	0	0	0	0	0	3	0	1	0	0	0	0	0	0	1	0	4	10	0	10	1	0	0	0	4	0	0	0	0	0	0	0	0	1	2	1	1	1	2	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	3	1	0	5	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	1	14	0	1	0	0	1	1	0	0	0	1	0	0	0	0	1	0	2	0	1	0	0	0	0	0	0	1	1	1	0	0	1	1	1	1	0	5	0	0	0	0	0	0	1	1	0	1	0	1	5	1	0	3	1
2020-09-05	0	0	1	0	0	1	0	0	1	0	28	0	0	3	0	0	0	1	1	0	0	1	0	0	1	0	1	0	13	0	1	0	0	4	0	0	0	1	1	0	1	0	1	0	0	2	0	0	0	0	0	0	4	1	0	1	0	0	0	0	0	3	0	1	0	0	0	0	0	0	1	0	4	10	0	10	1	0	0	0	4	0	0	0	0	0	0	0	0	1	2	1	1	1	2	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	3	1	0	5	0	0	0	0	0	0	7	1	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	1	14	0	1	0	0	1	1	0	0	0	1	0	0	0	0	1	0	2	0	1	0	0	0	0	0	0	1	1	1	0	0	1	1	1	1	0	5	0	0	0	0	0	0	1	1	0	1	0	1	5	1	0	3	1
2020-09-06	0	0	1	1	0	1	0	0	1	0	28	0	0	3	0	0	1	1	1	0	0	1	0	1	1	0	1	1	13	0	1	0	0	4	0	0	0	1	1	0	1	0	1	0	0	2	0	0	0	0	0	0	4	1	0	1	0	0	0	0	0	3	0	1	0	0	1	0	0	0	1	0	4	10	0	10	1	0	0	0	4	0	0	0	0	0	0	0	0	1	2	1	1	1	2	1	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	1	3	1	0	5	0	0	0	0	0	0	7	1	0	1	0	0	1	0	1	0	1	0	0	0	1	0	0	1	14	1	1	0	0	1	1	1	0	0	1	0	0	0	0	1	0	2	0	1	0	0	0	0	0	0	1	1	1	0	1	1	1	1	1	0	5	0	0	0	0	1	0	1	1	0	1	0	1	5	1	0	3	1
2020-09-07	0	0	1	1	0	1	0	0	1	0	28	0	0	3	0	0	1	1	1	0	0	1	0	1	1	0	1	1	13	0	1	0	0	4	0	0	0	1	1	0	1	1	1	0	0	2	0	1	0	0	0	0	4	1	0	1	0	0	0	0	0	3	0	1	0	0	1	0	0	0	1	0	4	10	0	10	1	0	0	0	4	0	0	1	0	0	0	0	0	1	2	1	1	1	2	1	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	1	3	1	0	5	0	0	0	0	0	1	7	1	0	1	0	0	1	0	1	0	1	0	0	13	1	0	0	1	14	1	1	0	0	1	1	1	0	0	1	0	0	0	0	1	0	2	0	1	0	0	0	0	0	0	1	1	1	0	1	1	1	1	1	0	5	0	0	0	0	1	0	1	1	0	1	0	1	5	1	0	3	1
2020-09-08	0	0	1	1	0	1	1	0	1	0	28	1	0	3	0	0	1	1	1	0	0	1	0	1	1	0	1	1	13	0	1	0	0	4	0	0	0	1	1	0	1	1	1	0	0	2	0	1	0	0	0	0	4	1	0	1	0	0	0	0	0	3	0	1	0	0	1	0	0	0	1	0	4	10	0	10	1	0	0	0	4	0	0	1	0	0	0	0	0	1	2	1	1	1	2	1	0	1	0	1	0	1	0	1	0	0	0	0	0	0	0	1	3	1	0	5	0	0	0	0	0	1	7	1	0	1	0	1	1	0	1	0	1	1	0	13	1	0	0	1	14	1	1	0	0	1	1	1	0	0	1	0	0	1	0	1	0	2	0	1	0	0	0	0	0	0	1	1	1	0	1	1	1	1	1	0	5	0	0	0	0	1	0	1	1	0	1	0	1	5	1	0	3	1
2020-09-09	0	0	1	1	0	1	1	0	1	0	28	1	0	3	0	0	1	1	1	0	0	1	0	1	1	0	1	1	13	0	1	0	0	4	0	0	0	1	1	0	1	1	1	0	0	2	0	1	0	0	0	0	4	1	0	1	0	0	0	0	0	3	0	1	0	0	1	0	0	0	1	0	4	10	0	10	1	0	0	0	4	0	0	1	0	0	0	0	0	1	2	1	1	1	2	1	0	1	0	1	0	1	0	1	0	0	0	0	0	0	0	1	3	1	0	5	0	0	0	0	0	1	7	1	0	1	0	1	1	0	1	0	1	1	0	13	1	0	0	1	14	1	1	0	0	1	1	1	0	0	1	0	0	1	0	1	0	2	0	1	0	0	0	0	0	0	1	1	1	0	1	1	1	1	1	0	5	0	0	0	0	1	0	1	1	0	1	0	1	5	1	0	3	1
2020-09-10	0	0	1	1	1	1	1	0	1	0	28	1	0	8	0	0	1	1	2	0	0	1	0	1	1	0	1	1	13	0	1	0	0	4	0	0	0	1	1	0	1	1	1	0	0	2	0	1	0	0	0	0	4	1	1	1	0	0	0	0	0	3	0	1	0	0	1	0	1	1	1	0	4	10	1	10	1	0	0	0	4	0	0	1	0	0	0	0	0	1	2	1	1	1	2	1	0	1	0	1	0	1	1	1	0	0	0	0	0	0	0	1	3	1	0	7	0	0	0	0	0	1	7	1	0	1	0	1	1	0	1	0	1	1	0	14	1	0	0	1	14	1	1	0	0	1	1	1	0	0	1	0	0	1	0	1	0	3	0	1	0	0	0	0	0	0	1	1	1	0	1	1	1	1	1	0	5	0	0	0	0	1	0	1	1	0	1	0	1	5	1	0	3	1
2020-09-11	0	0	1	1	4	1	1	1	1	0	28	1	0	9	0	0	1	1	2	0	0	1	0	1	1	0	3	1	13	0	1	0	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	2	2	0	1	0	4	1	1	1	0	0	0	0	1	3	0	1	1	0	1	0	1	1	1	0	4	10	1	10	1	0	0	0	4	0	1	1	0	0	0	0	1	1	2	2	1	1	2	1	0	1	1	1	0	1	1	1	0	0	0	0	0	0	1	1	3	1	1	8	0	0	0	0	0	1	7	1	1	1	0	1	2	1	1	0	1	1	0	16	2	0	0	1	14	1	1	0	0	1	1	1	0	0	1	1	0	1	0	1	0	4	0	1	0	0	0	0	0	1	1	1	1	0	1	1	1	1	1	1	5	0	0	0	0	1	0	1	1	0	1	0	1	5	1	1	3	1
2020-09-12	0	0	1	1	4	1	1	1	1	0	28	1	0	9	0	0	1	1	2	0	0	1	0	1	1	0	3	1	13	0	1	0	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	2	2	0	1	0	4	1	1	1	0	0	0	0	1	3	0	1	1	0	1	0	1	1	1	0	4	10	1	10	1	1	0	0	4	0	1	1	0	0	0	0	1	1	2	2	1	1	2	1	0	1	1	1	0	1	1	1	0	0	0	0	0	0	1	1	3	1	1	8	0	0	0	0	0	1	7	1	1	1	0	1	2	1	1	0	1	1	0	16	2	0	0	1	14	1	1	0	0	1	1	1	0	0	1	1	0	1	0	1	0	4	0	1	0	0	0	0	0	1	1	1	1	0	1	1	1	1	1	1	5	0	0	0	0	1	0	1	1	0	1	0	1	5	1	1	3	1
2020-09-13	0	0	1	1	4	1	1	1	1	0	28	1	0	9	0	0	1	1	2	0	0	1	0	1	1	0	3	1	13	0	1	0	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	3	2	0	1	0	4	1	1	1	0	0	0	0	1	3	0	1	1	0	1	0	1	1	1	0	4	10	1	10	1	1	0	0	4	0	1	1	0	0	0	0	1	1	2	2	1	1	2	1	0	1	1	1	0	1	1	1	0	0	0	0	0	0	1	1	3	1	1	8	0	0	0	0	0	1	7	1	1	1	0	1	2	1	1	0	1	1	0	16	2	0	0	1	14	1	1	0	0	1	1	1	0	0	1	1	0	1	0	1	0	4	0	1	0	0	0	0	0	1	1	1	1	0	1	1	1	1	1	1	5	0	0	0	0	1	0	1	1	0	1	0	1	5	1	1	3	1
2020-09-14	0	0	1	1	4	1	1	1	1	0	28	1	0	9	0	0	1	1	2	0	0	1	0	1	1	0	3	1	13	0	1	0	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	3	2	0	1	0	4	1	1	1	1	0	0	0	1	3	0	1	1	0	1	0	1	1	1	0	4	10	1	10	1	1	0	0	4	0	1	1	0	0	0	0	1	1	2	2	1	1	2	1	0	1	1	1	0	1	1	1	1	0	0	0	0	0	1	1	3	1	1	8	0	0	0	0	0	1	7	1	1	1	0	1	2	1	1	0	1	1	1	18	2	0	0	1	21	1	1	0	0	1	1	1	0	0	1	1	0	1	0	1	0	5	0	1	0	0	0	0	0	1	1	1	1	0	1	1	1	1	1	1	5	0	0	0	0	1	0	1	1	0	1	0	1	5	1	1	3	1
2020-09-15	0	0	1	1	4	1	1	1	1	1	28	1	0	9	0	0	1	1	2	0	0	1	0	1	1	0	3	1	13	0	1	0	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	3	2	0	2	0	4	1	1	1	1	1	0	1	1	3	0	1	1	1	1	1	1	1	1	0	4	10	1	10	1	1	0	0	4	0	1	1	0	1	0	0	1	1	2	3	1	1	2	1	0	1	1	1	0	1	1	1	2	0	0	1	0	0	1	1	3	1	2	8	0	0	1	0	0	1	7	1	1	1	1	1	2	1	1	0	1	1	1	23	2	0	1	6	21	1	1	0	0	1	1	1	0	1	1	1	1	1	0	1	0	5	0	1	1	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	5	0	0	0	0	1	0	1	1	0	1	0	1	5	1	1	3	1
2020-09-16	0	1	1	1	4	1	1	1	1	1	28	1	1	9	1	0	1	2	2	0	0	1	0	1	1	0	3	1	13	0	1	0	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	6	2	0	2	0	4	1	1	1	1	1	0	1	1	3	0	1	1	1	1	1	1	1	1	0	4	10	1	10	1	1	0	0	4	1	1	1	0	1	0	0	1	1	2	3	1	1	2	1	0	1	1	1	0	2	1	1	2	0	0	1	0	0	1	1	3	1	2	8	0	0	1	0	1	1	7	1	1	1	1	1	2	1	1	0	1	1	1	27	2	0	1	6	21	1	1	1	1	1	1	1	0	1	1	1	1	1	2	1	1	5	0	1	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	5	0	0	0	0	1	0	1	1	0	1	0	1	5	1	1	3	1
2020-09-17	0	1	1	1	4	1	1	1	1	1	28	1	1	9	1	0	1	2	2	0	0	1	0	1	1	0	3	1	13	0	1	0	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	6	2	0	2	0	4	1	1	1	1	1	0	1	1	3	0	1	1	1	1	1	1	1	1	1	4	10	1	10	1	1	1	0	4	1	1	1	0	1	0	0	1	1	2	3	1	1	2	1	0	1	1	1	1	2	1	1	2	1	0	1	1	0	1	1	3	1	2	16	0	0	1	0	2	1	7	1	1	1	1	1	2	1	1	0	1	1	1	27	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	5	0	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	5	0	1	1	1	1	0	1	1	0	1	0	1	5	1	1	3	1
2020-09-18	0	1	1	1	4	1	1	1	1	1	28	1	1	9	1	0	1	2	2	0	0	1	0	1	1	0	3	1	13	0	1	6	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	6	2	0	2	0	4	1	1	1	1	1	0	1	1	3	0	1	1	1	2	1	1	1	1	1	4	10	1	10	1	1	1	1	4	1	1	1	0	1	0	0	1	1	2	3	1	1	3	1	0	1	1	1	1	2	1	1	2	1	0	1	1	0	1	1	3	1	2	16	0	0	1	0	2	1	7	1	1	1	1	1	2	1	1	1	1	1	1	28	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	5	0	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	5	0	1	1	1	1	0	1	1	1	1	0	1	5	1	1	3	1
2020-09-19	0	1	1	1	4	1	1	1	1	1	28	1	1	9	1	0	1	2	2	0	0	1	0	1	1	0	3	1	13	0	1	6	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	6	2	0	2	0	4	1	1	1	1	1	0	1	1	3	0	1	1	1	2	1	1	1	1	1	4	10	1	10	1	1	1	1	4	1	1	1	0	1	0	0	1	1	2	3	1	1	3	1	0	1	1	1	1	2	1	1	2	1	0	1	1	0	1	1	3	1	2	16	0	0	1	0	2	1	7	1	1	1	1	1	2	1	1	1	1	1	1	29	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	5	0	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	5	0	1	1	1	1	0	1	1	1	1	0	1	5	1	1	3	1
2020-09-20	0	1	1	1	4	1	1	1	1	1	28	1	1	9	1	0	1	2	2	0	0	1	0	1	1	0	3	1	13	0	1	6	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	6	2	0	2	0	4	1	1	1	1	1	0	1	1	3	0	1	1	1	2	1	1	1	1	1	4	10	1	10	1	1	1	1	4	1	1	1	0	1	0	0	1	1	2	3	1	1	3	1	0	1	1	1	1	2	1	1	2	1	0	1	1	0	1	1	3	1	2	16	0	0	1	0	2	1	7	1	1	1	1	1	2	1	1	1	1	1	1	29	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	5	0	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	5	0	1	1	1	1	0	1	1	1	1	0	1	5	1	1	3	1
2020-09-21	0	1	1	1	4	1	1	1	1	1	28	1	1	9	1	0	1	2	2	0	0	1	0	1	1	0	3	1	13	0	1	6	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	6	2	0	2	0	4	1	1	1	1	1	0	1	1	3	0	1	1	1	2	1	1	1	1	1	4	10	1	10	1	1	1	1	4	1	1	1	0	1	0	0	1	1	2	3	1	1	3	1	0	1	1	1	1	2	1	1	2	1	0	1	1	0	1	1	3	1	2	16	0	0	1	0	2	1	7	1	1	1	1	1	2	1	1	1	1	1	1	29	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	5	0	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	5	0	1	1	1	1	0	1	1	1	1	0	1	5	1	1	3	1
2020-09-22	0	1	1	1	4	1	1	1	1	1	28	1	1	9	1	0	1	2	2	0	0	1	0	1	1	0	3	1	13	0	1	6	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	6	2	0	2	0	4	1	1	1	1	1	0	1	1	3	0	1	1	1	2	1	1	1	1	1	4	10	1	10	1	1	1	1	4	1	1	1	0	1	0	0	1	1	2	3	1	1	3	1	0	1	1	1	1	2	1	1	2	1	0	1	1	0	1	1	3	1	2	16	0	0	1	0	2	1	7	1	1	1	1	1	2	1	1	1	1	1	1	29	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	5	0	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	5	0	1	1	1	1	0	1	1	1	1	0	1	5	1	1	3	1
2020-09-23	0	1	1	1	4	1	1	1	1	1	28	1	1	9	1	0	1	2	2	0	0	1	0	1	1	0	3	1	13	0	1	6	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	6	2	0	2	0	4	1	1	1	1	1	0	1	1	3	0	1	1	1	2	1	1	1	1	1	4	10	1	10	1	1	1	1	4	1	1	1	0	1	0	0	1	1	2	3	1	1	3	1	0	1	1	1	1	2	1	1	2	1	0	1	1	0	1	1	3	1	2	16	0	0	1	0	2	1	7	1	1	1	1	1	2	1	1	1	1	1	1	29	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	5	0	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	5	0	1	1	1	1	0	1	1	1	1	0	1	5	1	1	3	1
2020-09-24	0	1	1	1	4	1	1	1	1	1	28	1	1	9	1	0	1	2	2	0	0	1	0	1	1	0	3	1	13	0	1	6	1	4	0	0	0	1	1	1	1	1	1	0	0	2	1	6	2	0	2	0	4	1	1	1	1	1	0	1	1	3	0	1	1	1	2	1	1	1	1	1	4	10	1	10	1	1	1	1	4	1	1	1	0	1	0	0	1	1	2	3	1	1	3	1	0	1	1	1	1	2	1	1	2	1	0	1	1	0	1	1	3	1	2	16	0	0	1	0	2	1	7	1	1	1	1	1	2	1	1	1	1	1	1	29	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	5	0	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	5	0	1	1	1	1	0	1	1	1	1	0	1	5	1	1	3	1
2020-09-25	1	1	1	1	4	1	1	1	1	1	29	1	1	10	1	0	1	2	2	0	1	1	0	1	1	0	3	1	13	0	1	6	1	4	0	0	0	1	1	1	1	1	1	1	0	2	1	6	2	0	2	0	5	1	1	1	1	1	0	1	1	3	0	1	1	1	2	1	1	1	1	1	4	10	1	11	1	1	1	1	4	2	1	1	0	1	1	0	1	1	2	3	1	1	3	1	0	1	1	1	1	2	1	1	2	2	0	1	1	0	1	1	3	1	2	16	0	0	1	1	2	1	7	1	1	1	1	1	2	1	1	1	1	1	2	32	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	6	0	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	6	0	1	1	1	1	1	1	1	1	1	0	1	6	1	1	3	1
2020-09-26	1	1	1	1	4	1	1	1	1	1	29	1	1	10	1	0	1	2	2	0	1	1	0	1	1	0	3	1	13	0	1	6	1	4	0	0	0	1	1	1	1	1	1	1	0	2	1	6	2	0	2	0	5	1	1	1	1	1	0	1	1	3	0	1	1	1	2	1	1	1	1	1	4	10	1	11	1	1	1	1	4	2	1	1	0	1	1	0	1	1	2	3	1	1	3	1	0	1	1	1	1	2	1	1	2	2	0	1	1	0	1	1	3	1	2	16	0	0	1	1	2	1	7	1	1	1	1	1	2	1	1	1	1	1	2	32	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	6	0	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	6	0	1	1	1	1	1	1	1	1	1	0	1	6	1	1	3	1
2020-09-27	1	1	1	1	4	1	1	1	1	1	29	1	1	10	1	0	1	2	2	0	1	1	0	1	1	0	3	1	13	0	1	6	1	4	0	0	0	1	1	1	1	1	1	1	0	2	1	6	2	0	2	0	5	6	1	1	1	1	0	1	1	3	0	1	1	1	2	1	1	1	1	1	4	10	1	11	1	1	1	1	4	2	1	1	0	1	1	0	1	1	2	3	1	1	3	1	0	1	1	1	1	2	1	1	2	2	0	1	1	0	1	1	3	1	2	16	0	0	1	1	2	1	7	1	1	1	1	1	2	1	1	1	1	1	2	33	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	6	0	1	1	1	0	0	1	1	2	1	1	1	1	1	1	1	1	1	7	0	1	1	1	1	1	1	1	1	1	0	1	6	1	1	3	1
2020-09-28	1	1	1	1	4	1	1	1	1	1	29	1	1	10	1	0	1	2	2	0	1	1	0	1	1	0	3	1	13	0	1	6	1	4	0	0	1	1	1	1	1	1	1	1	0	2	1	6	2	1	2	0	5	6	1	1	12	1	0	1	1	3	0	1	1	1	2	1	1	1	1	1	5	10	1	11	1	1	1	1	4	2	1	1	1	1	1	0	1	1	2	3	1	1	3	1	0	1	1	1	1	2	1	1	3	2	0	1	1	0	1	1	3	1	2	16	0	0	1	1	2	1	7	1	1	1	1	1	2	1	1	1	1	1	2	33	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	6	0	1	1	1	0	0	1	1	2	1	1	1	1	1	1	1	1	1	7	0	1	1	1	1	1	1	1	1	1	0	1	6	1	1	3	1
2020-09-29	1	1	1	1	4	1	1	1	1	1	29	1	1	10	1	0	1	2	2	0	1	1	0	1	1	0	3	1	13	0	1	6	1	4	0	0	1	1	1	1	1	1	1	1	0	2	1	6	2	1	2	0	5	6	1	1	12	1	0	1	1	3	0	1	1	1	2	1	1	1	1	1	5	10	1	11	1	1	1	1	4	2	1	1	1	1	1	0	1	1	2	3	1	1	3	1	0	1	1	1	1	2	1	1	3	2	0	1	1	0	1	1	3	1	2	16	0	0	1	1	2	1	7	1	1	1	1	1	2	1	1	1	1	1	2	33	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	6	0	1	1	1	0	0	1	1	2	1	1	1	1	1	1	1	1	1	7	0	1	1	1	1	1	1	1	1	1	0	1	6	1	1	3	1
2020-09-30	1	1	1	1	4	1	1	1	1	1	29	1	1	10	1	1	1	2	3	1	1	1	1	1	1	1	3	1	13	1	1	6	1	4	0	0	1	1	1	1	1	1	1	1	1	2	1	6	2	1	2	0	5	6	1	1	12	1	0	1	1	3	1	1	1	1	2	1	1	1	1	1	6	10	1	11	1	1	1	1	4	2	2	1	1	1	1	1	1	1	2	3	1	1	3	1	1	1	1	1	1	2	1	1	3	2	0	1	1	0	1	1	3	1	2	16	0	0	1	1	2	1	7	1	1	1	1	1	2	1	1	1	1	1	2	33	2	0	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	6	0	1	1	1	1	0	1	1	2	1	1	1	1	1	1	1	1	1	7	0	1	1	1	1	1	1	1	1	1	1	1	6	1	1	3	1
2020-10-01	2	1	1	1	4	1	1	1	1	1	29	1	1	11	1	1	1	2	3	1	1	1	1	1	1	1	3	1	13	1	1	6	1	4	0	0	1	1	1	1	1	1	1	2	1	2	1	7	2	1	2	1	5	6	1	1	13	1	0	1	1	3	1	1	1	1	3	1	1	1	1	1	6	10	1	11	1	1	1	1	4	2	2	1	1	1	1	1	1	2	2	3	1	1	3	1	1	1	1	1	1	2	1	1	4	2	0	1	1	0	1	1	3	1	2	16	1	1	1	1	2	1	7	1	1	1	1	1	2	1	1	1	1	1	2	34	2	1	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	6	4	1	1	1	1	0	1	1	2	1	1	1	1	1	1	1	1	1	7	0	1	1	1	1	1	1	1	1	1	1	1	6	1	1	3	1
2020-10-02	2	1	1	1	4	1	1	1	1	1	29	1	1	11	1	1	1	2	3	1	1	1	1	1	1	1	3	1	13	1	1	6	1	4	0	0	1	1	1	1	1	1	1	2	1	2	1	7	2	1	2	1	5	6	1	1	13	1	0	1	1	3	1	1	1	1	3	1	1	1	1	1	6	10	1	11	1	1	1	1	4	2	2	1	1	1	1	1	1	2	2	3	1	1	3	1	1	1	1	1	1	2	1	1	4	2	0	1	1	0	1	1	3	1	2	16	1	1	1	1	2	1	7	1	1	1	1	1	2	1	1	1	1	1	2	34	2	1	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	6	4	1	1	1	1	0	1	1	2	1	1	1	1	1	1	1	1	1	7	0	1	1	1	1	1	1	1	1	1	1	1	6	1	1	3	1
2020-10-03	2	1	1	1	4	1	1	1	1	1	29	1	1	11	1	1	1	2	3	1	1	1	1	1	1	1	3	1	13	1	1	6	1	4	0	0	1	1	1	1	1	1	1	2	1	2	1	7	2	1	2	1	5	6	1	1	13	1	0	1	1	3	1	1	1	1	3	1	1	1	1	1	6	10	1	11	1	1	1	1	4	2	2	1	1	1	1	1	1	2	2	3	1	1	3	1	1	1	1	1	1	2	1	1	4	2	0	1	1	0	1	1	3	1	2	16	1	1	1	1	2	1	7	1	1	1	1	1	2	1	1	1	1	1	2	34	2	1	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	6	4	1	1	1	1	0	1	1	2	1	1	1	1	1	1	1	1	1	7	0	1	1	1	1	1	1	1	1	1	1	1	6	1	1	3	1
2020-10-04	2	1	1	1	4	1	1	1	1	1	29	1	1	11	1	1	1	2	3	1	1	1	1	1	1	1	3	1	13	1	1	6	1	4	0	0	1	1	1	1	1	1	1	2	1	2	1	7	2	1	2	1	5	6	1	1	13	1	0	1	1	3	1	1	1	1	3	1	1	1	1	1	6	10	1	11	1	1	1	1	4	2	2	1	1	1	1	1	1	2	2	3	1	1	3	1	1	1	1	1	1	2	1	1	4	2	0	1	1	0	1	1	3	1	2	16	1	1	1	1	2	1	7	1	1	1	1	1	2	1	1	1	1	1	2	34	2	1	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	6	4	1	1	1	1	0	1	1	2	1	1	1	1	1	1	1	1	1	7	0	1	1	1	1	1	1	1	1	1	1	1	6	1	1	3	1
2020-10-05	2	1	1	1	4	1	1	1	1	1	29	1	1	11	1	1	1	2	3	1	1	1	1	1	1	1	3	1	13	1	1	6	1	4	1	1	1	1	1	1	1	1	1	2	1	2	1	8	2	1	2	1	5	6	1	1	13	1	1	1	1	3	1	1	1	1	3	1	1	1	1	1	6	10	1	19	1	1	1	1	4	2	2	1	1	1	1	1	1	2	2	3	1	1	3	1	1	1	1	1	1	2	1	3	4	2	1	1	1	1	1	1	3	1	2	16	1	1	1	1	2	1	7	1	2	1	1	1	2	1	1	1	1	1	2	36	2	1	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	6	4	1	1	1	1	1	1	1	2	1	1	1	1	1	1	1	1	1	7	1	1	1	1	1	2	1	1	1	1	1	1	6	1	1	3	1
2020-10-06	2	1	1	1	4	1	1	1	1	1	29	1	1	11	1	1	1	2	3	1	1	1	1	1	1	2	3	1	13	1	1	6	1	4	1	1	1	1	1	1	1	1	1	2	1	2	1	8	2	1	2	1	5	6	1	1	13	1	1	1	1	3	1	1	1	1	4	1	1	1	1	1	6	10	1	19	1	1	1	1	4	2	2	1	1	1	1	1	1	2	2	3	1	1	3	1	1	1	1	1	1	2	1	4	4	2	1	1	1	1	1	1	3	1	2	21	1	1	1	1	2	1	7	1	2	1	1	1	2	1	1	1	1	1	2	37	2	1	1	6	21	1	1	1	1	1	1	1	1	1	1	1	1	1	2	1	1	6	4	1	1	1	1	1	1	1	2	1	1	1	1	1	1	1	1	1	7	1	1	1	1	1	2	1	1	1	1	1	1	6	1	1	3	1

State Frequency

state_tally

	State_abbv	State	Count	Text
0	CA	California	106	106 applications in California.
1	WA	Washington	44	44 applications in Washington.
2	NY	New York	40	40 applications in New York.
3	TX	Texas	34	34 applications in Texas.
4	VA	Virginia	31	31 applications in Virginia.
5	PA	Pennsylvania	27	27 applications in Pennsylvania.
6	IL	Illinois	24	24 applications in Illinois.
7	MA	Massachusetts	21	21 applications in Massachusetts.
8	NC	North Carolina	20	20 applications in North Carolina.
9	GA	Georgia	17	17 applications in Georgia.
10	MD	Maryland	15	15 applications in Maryland.
11	DC	District Of Columbia	14	14 applications in District Of Columbia.
12	CO	Colorado	11	11 applications in Colorado.
13	AZ	Arizona	8	8 applications in Arizona.
14	OH	Ohio	7	7 applications in Ohio.
15	NJ	New Jersey	6	6 applications in New Jersey.
16	CT	Connecticut	6	6 applications in Connecticut.
17	MI	Michigan	6	6 applications in Michigan.
18	MN	Minnesota	5	5 applications in Minnesota.
19	OR	Oregon	3	3 applications in Oregon.
20	KY	Kentucky	3	3 applications in Kentucky.
21	TN	Tennessee	3	3 applications in Tennessee.
22	IN	Indiana	3	3 applications in Indiana.
23	MO	Missouri	2	2 applications in Missouri.
24	RI	Rhode Island	2	2 applications in Rhode Island.
25	SC	South Carolina	2	2 applications in South Carolina.
26	UT	Utah	1	1 application in Utah.
27	AR	Arkansas	1	1 application in Arkansas.
28	KS	Kansas	1	1 application in Kansas.
29	ID	Idaho	1	1 application in Idaho.
30	FL	Florida	1	1 application in Florida.
31	DE	Delaware	1	1 application in Delaware.
32	WV	West Virginia	1	1 application in West Virginia.